






'''simple docstring'''







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase        =							"\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase        =							"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase        =							"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       def remove_articles(lowercase_       :  List[str]   ):
              a_						     =			re.compile(R'\b(a|an|the)\b'   ,       re.UNICODE   )
              return re.sub(lowercase_   ,       ' '   ,       lowercase_   )

       def white_space_fix(lowercase_       :  List[Any]   ):
              return " ".join(text.split()   )

       def remove_punc(lowercase_       :  Any   ):
              a_						     =			set(string.punctuation   )
              return "".join(ch for ch in text if ch not in exclude   )

       def lower(lowercase_       :  int   ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase_   )   )   )   )


def       __UpperCamelCase					(      lowercase_       :  List[str]   ,       lowercase_       :  Any   ):
       """simple docstring"""




       return int(normalize_answer(lowercase_   ) == normalize_answer(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  Dict   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[any(compute_exact(lowercase_   ,       lowercase_   ) for ref in refs   ) for pred, refs in zip(lowercase_   ,       lowercase_   )]
       return (sum(lowercase_   ) / len(lowercase_   )) * 100


def       __UpperCamelCase					(      lowercase_       :  Any   ,       lowercase_       :  int   ,       lowercase_       :  List[Any]   ,       lowercase_       :  List[str]   ):
       """simple docstring"""




       a_						     =			[rgram for rgrams in rgramslist for rgram in rgrams]
       a_						     =			Counter(lowercase_   )

       a_						     =			Counter(lowercase_   )
       a_						     =			Counter()
       for sgram, scount in sgramcounter.items():
              a_						     =			scount * numref

       a_						     =			Counter(lowercase_   )
       a_						     =			Counter()
       for cgram, ccount in cgramcounter.items():
              a_						     =			ccount * numref

       # KEEP
       a_						     =			sgramcounter_rep & cgramcounter_rep
       a_						     =			keepgramcounter_rep & rgramcounter
       a_						     =			sgramcounter_rep & rgramcounter

       a_						     =			0
       a_						     =			0
       for keepgram in keepgramcountergood_rep:
              keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
              # Fix an alleged bug [2] in the keep score computation.
              # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
              keeptmpscorea += keepgramcountergood_rep[keepgram]
       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       #      a target exactly.
       a_						     =			1
       a_						     =			1
       if len(lowercase_   ) > 0:
              a_						     =			keeptmpscorea / len(lowercase_   )
       if len(lowercase_   ) > 0:
              # Fix an alleged bug [2] in the keep score computation.
              # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
              a_						     =			keeptmpscorea / sum(keepgramcounterall_rep.values()   )
       a_						     =			0
       if keepscore_precision > 0 or keepscore_recall > 0:
              a_						     =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

       # DELETION
       a_						     =			sgramcounter_rep - cgramcounter_rep
       a_						     =			delgramcounter_rep - rgramcounter
       a_						     =			sgramcounter_rep - rgramcounter
       a_						     =			0
       a_						     =			0
       for delgram in delgramcountergood_rep:
              deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
              deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       # a target exactly.
       a_						     =			1
       if len(lowercase_   ) > 0:
              a_						     =			deltmpscorea / len(lowercase_   )

       # ADDITION
       a_						     =			set(lowercase_   ) - set(lowercase_   )
       a_						     =			set(lowercase_   ) & set(lowercase_   )
       a_						     =			set(lowercase_   ) - set(lowercase_   )

       a_						     =			0
       for addgram in addgramcountergood:
              addtmpscore += 1

       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       # a target exactly.
       a_						     =			1
       a_						     =			1
       if len(lowercase_   ) > 0:
              a_						     =			addtmpscore / len(lowercase_   )
       if len(lowercase_   ) > 0:
              a_						     =			addtmpscore / len(lowercase_   )
       a_						     =			0
       if addscore_precision > 0 or addscore_recall > 0:
              a_						     =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)


def       __UpperCamelCase					(      lowercase_       :  int   ,       lowercase_       :  Union[str, Any]   ,       lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       a_						     =			len(lowercase_   )

       a_						     =			ssent.split(' '   )
       a_						     =			csent.split(' '   )
       a_						     =			[]
       a_						     =			[]
       a_						     =			[]
       a_						     =			[]
       a_						     =			[]
       a_						     =			[]

       a_						     =			[]
       a_						     =			[]
       a_						     =			[]
       a_						     =			[]
       for rsent in rsents:
              a_						     =			rsent.split(' '   )
              a_						     =			[]
              a_						     =			[]
              a_						     =			[]
              ragramslist.append(lowercase_   )
              for i in range(0   ,       len(lowercase_   ) - 1   ):
                     if i < len(lowercase_   ) - 1:
                            a_						     =			ragrams[i] + ' ' + ragrams[i + 1]
                            ragrams.append(lowercase_   )
                     if i < len(lowercase_   ) - 2:
                            a_						     =			ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                            ragrams.append(lowercase_   )
                     if i < len(lowercase_   ) - 3:
                            a_						     =			ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                            ragrams.append(lowercase_   )
              ragramslist.append(lowercase_   )
              ragramslist.append(lowercase_   )
              ragramslist.append(lowercase_   )

       for i in range(0   ,       len(lowercase_   ) - 1   ):
              if i < len(lowercase_   ) - 1:
                     a_						     =			sagrams[i] + ' ' + sagrams[i + 1]
                     sagrams.append(lowercase_   )
              if i < len(lowercase_   ) - 2:
                     a_						     =			sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                     sagrams.append(lowercase_   )
              if i < len(lowercase_   ) - 3:
                     a_						     =			sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                     sagrams.append(lowercase_   )

       for i in range(0   ,       len(lowercase_   ) - 1   ):
              if i < len(lowercase_   ) - 1:
                     a_						     =			cagrams[i] + ' ' + cagrams[i + 1]
                     cagrams.append(lowercase_   )
              if i < len(lowercase_   ) - 2:
                     a_						     =			cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
                     cagrams.append(lowercase_   )
              if i < len(lowercase_   ) - 3:
                     a_						     =			cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
                     cagrams.append(lowercase_   )

       ((a_)     ,	(a_)     ,	(a_))						     =			SARIngram(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )
       ((a_)     ,	(a_)     ,	(a_))						     =			SARIngram(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )
       ((a_)     ,	(a_)     ,	(a_))						     =			SARIngram(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )
       ((a_)     ,	(a_)     ,	(a_))						     =			SARIngram(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )
       a_						     =			sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
       a_						     =			sum([delascore, delascore, delascore, delascore]   ) / 4
       a_						     =			sum([addascore, addascore, addascore, addascore]   ) / 4
       a_						     =			(avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  bool = True   ,       lowercase_       :  str = "13a"   ,       lowercase_       :  bool = True   ):
       """simple docstring"""




       if lowercase:
              a_						     =			sentence.lower()

       if tokenizer in ["13a", "intl"]:
              if version.parse(sacrebleu.__version__   ).major >= 2:
                     a_						     =			sacrebleu.metrics.bleu._get_tokenizer(lowercase_   )()(lowercase_   )
              else:
                     a_						     =			sacrebleu.TOKENIZERS[tokenizer]()(lowercase_   )
       elif tokenizer == "moses":
              a_						     =			sacremoses.MosesTokenizer().tokenize(lowercase_   ,       return_str=lowercase_   ,       escape=lowercase_   )
       elif tokenizer == "penn":
              a_						     =			sacremoses.MosesTokenizer().penn_tokenize(lowercase_   ,       return_str=lowercase_   )
       else:
              a_						     =			sentence

       if not return_str:
              a_						     =			normalized_sent.split()

       return normalized_sent


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  int   ,       lowercase_       :  List[str]   ):
       """simple docstring"""




       if not (len(lowercase_   ) == len(lowercase_   ) == len(lowercase_   )):
              raise ValueError('Sources length must match predictions and references lengths.'   )
       a_						     =			0
       for src, pred, refs in zip(lowercase_   ,       lowercase_   ,       lowercase_   ):
              sari_score += SARIsent(normalize(lowercase_   )   ,       normalize(lowercase_   )   ,       [normalize(lowercase_   ) for sent in refs]   )
       a_						     =			sari_score / len(lowercase_   )
       return 100 * sari_score


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  Union[str, Any]   ,       lowercase_       :  List[str]="exp"   ,       lowercase_       :  List[Any]=None   ,       lowercase_       :  Any=False   ,       lowercase_       :  Dict=False   ,       lowercase_       :  Optional[Any]=False   ,       ):
       """simple docstring"""




       a_						     =			len(references[0]   )
       if any(len(lowercase_   ) != references_per_prediction for refs in references   ):
              raise ValueError('Sacrebleu requires the same number of references for each prediction'   )
       a_						     =			[[refs[i] for refs in references] for i in range(lowercase_   )]
       a_						     =			sacrebleu.corpus_bleu(
           lowercase_   ,       lowercase_   ,       smooth_method=lowercase_   ,       smooth_value=lowercase_   ,       force=lowercase_   ,       lowercase=lowercase_   ,       use_effective_order=lowercase_   ,       )
       return output.score




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,       _KWARGS_DESCRIPTION       )
class   __SCREAMING_SNAKE_CASE       (datasets.Metric       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         return datasets.MetricInfo(
             description=_DESCRIPTION   ,	citation=_CITATION   ,	inputs_description=_KWARGS_DESCRIPTION   ,	features=datasets.Features(
                 {
                     'predictions': datasets.Value('string'   ,	id='sequence'	),
                     'references': datasets.Sequence(datasets.Value('string'   ,	id='sequence'	)   ,	id='references'	),
                 }	)   ,	codebase_urls=[
                 'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
                 'https://github.com/cocoxu/simplification/blob/master/SARI.py',
                 'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
                 'https://github.com/mjpost/sacreBLEU',
             ]   ,	reference_urls=[
                 'https://www.aclweb.org/anthology/Q16-1029.pdf',
                 'https://github.com/mjpost/sacreBLEU',
                 'https://en.wikipedia.org/wiki/BLEU',
                 'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
             ]   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			{}
         result.update({'sari': compute_sari(sources=UpperCamelCase__   ,	predictions=UpperCamelCase__   ,	references=UpperCamelCase__	)}	)
         result.update({'sacrebleu': compute_sacrebleu(predictions=UpperCamelCase__   ,	references=UpperCamelCase__	)}	)
         result.update({'exact': compute_em(predictions=UpperCamelCase__   ,	references=UpperCamelCase__	)}	)
         return result












'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




